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80 lines
3.7 KiB
Markdown
80 lines
3.7 KiB
Markdown
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# Kafka
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The engine works with [Apache Kafka](http://kafka.apache.org/).
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Kafka lets you:
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- Publish or subscribe to data flows.
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- Organize fault-tolerant storage.
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- Process streams as they become available.
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```
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Kafka(broker_list, topic_list, group_name, format[, schema])
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```
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Parameters:
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- `broker_list` – A comma-separated list of brokers (`localhost:9092`).
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- `topic_list` – A list of Kafka topics (`my_topic`).
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- `group_name` – A group of Kafka consumers (`group1`). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.
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- `--format` – Message format. Uses the same notation as the SQL ` FORMAT` function, such as ` JSONEachRow`.
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- `schema` – An optional parameter that must be used if the format requires a schema definition. For example, [Cap'n Proto](https://capnproto.org/) requires the path to the schema file and the name of the root ` schema.capnp:Message` object.
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Example:
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```sql
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CREATE TABLE queue (
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timestamp UInt64,
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level String,
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message String
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) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
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SELECT * FROM queue LIMIT 5;
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```
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The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.
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Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. For more information, see [http://kafka.apache.org/intro](http://kafka.apache.org/intro).
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`SELECT` is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. For this purpose, the following was done:
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1. Use the engine to create a Kafka consumer and consider it a data stream.
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2. Create a table with the desired structure.
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3. Create a materialized view that converts data from the engine and puts it into a previously created table.
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When the `MATERIALIZED VIEW` joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using `SELECT`
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Example:
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```sql
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CREATE TABLE queue (
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timestamp UInt64,
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level String,
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message String
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) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');
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CREATE TABLE daily (
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day Date,
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level String,
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total UInt64
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) ENGINE = SummingMergeTree(day, (day, level), 8192);
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CREATE MATERIALIZED VIEW consumer TO daily
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AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total
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FROM queue GROUP BY day, level;
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SELECT level, sum(total) FROM daily GROUP BY level;
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```
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To improve performance, received messages are grouped into blocks the size of [max_block_size](../operations/settings/settings.md#settings-settings-max_insert_block_size). If the block wasn't formed within [ stream_flush_interval_ms](../operations/settings/settings.md#settings-settings_stream_flush_interval_ms) milliseconds, the data will be flushed to the table regardless of the completeness of the block.
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To stop receiving topic data or to change the conversion logic, detach the materialized view:
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```
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DETACH TABLE consumer;
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ATTACH MATERIALIZED VIEW consumer;
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```
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If you want to change the target table by using ` ALTER`materialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.
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